This paper explores the risks associated with deploying Large Language Models (LLMs) and evaluates current approaches to implementing guardrails and model alignment techniques. The authors highlight the inherent risks of LLMs, including bias, unsafe actions, dataset poisoning, lack of explainability, hallucinations, and non-reproducibility. They emphasize the need for guardrails to align LLMs with desired behaviors and mitigate potential harm. The paper discusses methods for evaluating intrinsic and extrinsic bias, the importance of fairness metrics, and the safety and reliability of agentic LLMs capable of real-world actions. It also presents technical strategies for securing LLMs, such as a layered protection model operating at external, secondary, and internal levels. System prompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to minimize bias and protect privacy are highlighted. Effective guardrail design requires a deep understanding of the LLM's intended use case, relevant regulations, and ethical considerations. The paper underscores the ongoing challenge of balancing competing requirements, such as accuracy and privacy, and the importance of continuous research and development to ensure the safe and responsible use of LLMs in real-world applications.This paper explores the risks associated with deploying Large Language Models (LLMs) and evaluates current approaches to implementing guardrails and model alignment techniques. The authors highlight the inherent risks of LLMs, including bias, unsafe actions, dataset poisoning, lack of explainability, hallucinations, and non-reproducibility. They emphasize the need for guardrails to align LLMs with desired behaviors and mitigate potential harm. The paper discusses methods for evaluating intrinsic and extrinsic bias, the importance of fairness metrics, and the safety and reliability of agentic LLMs capable of real-world actions. It also presents technical strategies for securing LLMs, such as a layered protection model operating at external, secondary, and internal levels. System prompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to minimize bias and protect privacy are highlighted. Effective guardrail design requires a deep understanding of the LLM's intended use case, relevant regulations, and ethical considerations. The paper underscores the ongoing challenge of balancing competing requirements, such as accuracy and privacy, and the importance of continuous research and development to ensure the safe and responsible use of LLMs in real-world applications.